In this paper, we make comprehensive studies for the bottom-up based methods, as this kind of methods has excellent speeds and owns more flexible representations for describing arbitrary-shape scene texts compared with top-down based methods.To reveal the advantages and disadvantages of the core techniques in existing methods, we propose a unified framework for the bottom-up based arbitrary-shape scene text detection, providing a common perspective for existing methods. Specifically, the proposed framework consists five consecutive operation modules.The first module is image pre-processing, which mainly refers to the data augmentation in the training stage and the image resizing in the testing stage.The second module is extracting visual features, using the backbone network pre-trained on ImageNet [1]}.The third is the feature fusion module, which fuses multi-scale features to obtain better feature representations.The fourth module is the prediction head, which outputs estimated parameters of describing arbitrary-shape texts. The fifth module refers to the post-processing, which is only utilized in the testing stage.We thus could use this unified framework to support all existing bottom-up based scene text detectors.Based on the proposed framework, we investigate the influence of the fourth module (namely, the prediction head) under the unified experimental settings (, the same image pre-processing, backbone network and feature fusion strategy), as almost bottom-up based arbitrary-shape scene text detectors pay attention on this module.With this investigation, we can clearly understand the strengths and weaknesses of existing bottom-up based arbitrary-shape scene text detection methods. Meanwhile, it also exposes some overlooked explorations and challenges, which could guide more flourishing studies in the future works.
